Abstract
Referring expression comprehension, which is the ability to locate language to an object in an image, plays an important role in creating common ground. Many models that fuse visual and linguistic features have been proposed. However, few models consider the fusion of linguistic features with multiple visual features with different sizes of receptive fields, though the proper size of the receptive field of visual features intuitively varies depending on expressions. In this paper, we introduce a neural network architecture that modulates visual features with varying sizes of receptive field by linguistic features. We evaluate our architecture on tasks related to referring expression comprehension in two visual dialogue games. The results show the advantages and broad applicability of our architecture. Source code is available at https://github.com/Alab-NII/lcfp .
Highlights
Referring expressions are a ubiquitous part of human communication (Krahmer and Van Deemter, 2012) that must be studied in order to create machines that work smoothly with humans
We introduce a neural network architecture for referring expression comprehension considering visual features with different sizes of the receptive fields, and evaluate it on the OneCommon task
To confirm the broad applicability of our architecture, we further evaluate it on another task, which is expected to require the ability of object category recognition more than OneCommon does because it uses photographs
Summary
Referring expressions are a ubiquitous part of human communication (Krahmer and Van Deemter, 2012) that must be studied in order to create machines that work smoothly with humans. Much effort has been taken to improve methods of creating visual common ground between machines, which have limited means of expression and knowledge about the real world, and humans, from the perspectives of both referring expression comprehension and generation (Moratz et al, 2002; Tenbrink and Moratz, 2003; Funakoshi et al, 2004, 2005, 2006; Fang et al, 2013). Many models have been proposed for referring expression comprehension so far. As image recognition matured, Guadarrama et al (2014) studied object retrieval methods based on category labels predicted by the recognition models. Models that fuse linguistic features with visual features using deep learning have been studied (Hu et al, 2016b,a; Anderson et al, 2018; Deng et al, 2018; Misra et al, 2018; Li et al, 2018; Yang et al, 2019a,b; Liu et al, 2019; Can et al, 2020)
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